






'''simple docstring'''





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_UpperCamelCase						:						Optional[Any]  =					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def __UpperCAmelCase   (					A					:			Tuple		)    ->   int:
			if "token" in model_name_or_path:
						return "rag_token"
			if "sequence" in model_name_or_path:
						return "rag_sequence"
			if "bart" in model_name_or_path:
						return "bart"
			return None
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			Dict			,       A					:			Any		)    ->   Optional[Any]:
			return max(metric_fn(A			,       A		) for gt in ground_truths		)
def __UpperCAmelCase   (					A					:			Any			,       A					:			Tuple			,       A					:			Dict		)    ->   Any:
			UpperCAmelCase_      :       Optional[Any]          =       [line.strip() for line in open(A			,       '''r'''		).readlines()]
			UpperCAmelCase_      :       Any          =       []

			if args.gold_data_mode == "qa":
						UpperCAmelCase_      :       Tuple          =       pd.read_csv(A			,       sep='''\t'''			,       header=A		)
						for answer_list in data[1]:
									UpperCAmelCase_      :       List[Any]          =       ast.literal_eval(A		)
									answers.append(A		)
			else:
						UpperCAmelCase_      :       List[Any]          =       [line.strip() for line in open(A			,       '''r'''		).readlines()]
						UpperCAmelCase_      :       List[Any]          =       [[reference] for reference in references]

			UpperCAmelCase_      :       Optional[Any]          =       0
			for prediction, ground_truths in zip(A			,       A		):
						total += 1
						em += metric_max_over_ground_truths(A			,       A			,       A		)
						fa += metric_max_over_ground_truths(A			,       A			,       A		)

			UpperCAmelCase_      :       List[str]          =       100.0 * em / total
			UpperCAmelCase_      :       Optional[Any]          =       100.0 * fa / total

			logger.info(F"F1: {fa:.2f}"		)
			logger.info(F"EM: {em:.2f}"		)
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			Tuple			,       A					:			List[str]		)    ->   str:
			UpperCAmelCase_      :       Optional[Any]          =       args.k
			UpperCAmelCase_      :       Dict          =       [line.strip() for line in open(A			,       '''r'''		).readlines()]
			UpperCAmelCase_      :       Union[str, Any]          =       [line.strip() for line in open(A			,       '''r'''		).readlines()]

			UpperCAmelCase_      :       Optional[Any]          =       0
			for hypo, reference in zip(A			,       A		):
						UpperCAmelCase_      :       Any          =       set(hypo.split('''\t'''		)[:k]		)
						UpperCAmelCase_      :       Tuple          =       set(reference.split('''\t'''		)		)
						total += 1
						em += len(hypo_provenance & ref_provenance		) / k

			UpperCAmelCase_      :       Union[str, Any]          =       100.0 * em / total
			logger.info(F"Precision@{k}: {em: .2f}"		)
def __UpperCAmelCase   (					A					:			str			,       A					:			Any			,       A					:			Tuple		)    ->   List[str]:
			def strip_title(A					:			Union[str, Any]		):
						if title.startswith('''"'''		):
									UpperCAmelCase_      :       Optional[int]          =       title[1:]
						if title.endswith('''"'''		):
									UpperCAmelCase_      :       List[str]          =       title[:-1]
						return title

			UpperCAmelCase_      :       Any          =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    A			,       return_tensors='''pt'''			,       padding=A			,       truncation=A			,       )['''input_ids'''].to(args.device		)

			UpperCAmelCase_      :       str          =       rag_model.rag.question_encoder(A		)
			UpperCAmelCase_      :       Any          =       question_enc_outputs[0]

			UpperCAmelCase_      :       List[str]          =       rag_model.retriever(
			    A			,       question_enc_pool_output.cpu().detach().to(torch.floataa		).numpy()			,       prefix=rag_model.rag.generator.config.prefix			,       n_docs=rag_model.config.n_docs			,       return_tensors='''pt'''			,       )
			UpperCAmelCase_      :       List[Any]          =       rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
			UpperCAmelCase_      :       Any          =       []
			for docs in all_docs:
						UpperCAmelCase_      :       List[Any]          =       [strip_title(A		) for title in docs['''title''']]
						provenance_strings.append('''\t'''.join(A		)		)
			return provenance_strings
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Tuple			,       A					:			Optional[int]		)    ->   Union[str, Any]:
			with torch.no_grad():
						UpperCAmelCase_      :       Dict          =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    A			,       return_tensors='''pt'''			,       padding=A			,       truncation=A		)

						UpperCAmelCase_      :       Optional[Any]          =       inputs_dict.input_ids.to(args.device		)
						UpperCAmelCase_      :       List[Any]          =       inputs_dict.attention_mask.to(args.device		)
						UpperCAmelCase_      :       int          =       rag_model.generate(  # rag_model overwrites generate
						    A			,       attention_mask=A			,       num_beams=args.num_beams			,       min_length=args.min_length			,       max_length=args.max_length			,       early_stopping=A			,       num_return_sequences=1			,       bad_words_ids=[[0, 0]]			,       )
						UpperCAmelCase_      :       List[str]          =       rag_model.retriever.generator_tokenizer.batch_decode(A			,       skip_special_tokens=A		)

						if args.print_predictions:
									for q, a in zip(A			,       A		):
												logger.info('''Q: {} - A: {}'''.format(A			,       A		)		)

						return answers
def __UpperCAmelCase   (					)    ->   List[str]:
			UpperCAmelCase_      :       Any          =       argparse.ArgumentParser()
			parser.add_argument(
			    '''--model_type'''			,       choices=['''rag_sequence''', '''rag_token''', '''bart''']			,       type=A			,       help=(
			        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
			        ''' model_name_or_path'''
			    )			,       )
			parser.add_argument(
			    '''--index_name'''			,       default=A			,       choices=['''exact''', '''compressed''', '''legacy''']			,       type=A			,       help='''RAG model retriever type'''			,       )
			parser.add_argument(
			    '''--index_path'''			,       default=A			,       type=A			,       help='''Path to the retrieval index'''			,       )
			parser.add_argument('''--n_docs'''			,       default=5			,       type=A			,       help='''Number of retrieved docs'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       default=A			,       type=A			,       required=A			,       help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''			,       )
			parser.add_argument(
			    '''--eval_mode'''			,       choices=['''e2e''', '''retrieval''']			,       default='''e2e'''			,       type=A			,       help=(
			        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
			        ''' precision@k.'''
			    )			,       )
			parser.add_argument('''--k'''			,       default=1			,       type=A			,       help='''k for the precision@k calculation'''		)
			parser.add_argument(
			    '''--evaluation_set'''			,       default=A			,       type=A			,       required=A			,       help='''Path to a file containing evaluation samples'''			,       )
			parser.add_argument(
			    '''--gold_data_path'''			,       default=A			,       type=A			,       required=A			,       help='''Path to a tab-separated file with gold samples'''			,       )
			parser.add_argument(
			    '''--gold_data_mode'''			,       default='''qa'''			,       type=A			,       choices=['''qa''', '''ans''']			,       help=(
			        '''Format of the gold data file'''
			        '''qa - a single line in the following format: question [tab] answer_list'''
			        '''ans - a single line of the gold file contains the expected answer string'''
			    )			,       )
			parser.add_argument(
			    '''--predictions_path'''			,       type=A			,       default='''predictions.txt'''			,       help='''Name of the predictions file, to be stored in the checkpoints directory'''			,       )
			parser.add_argument(
			    '''--eval_all_checkpoints'''			,       action='''store_true'''			,       help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''			,       )
			parser.add_argument(
			    '''--eval_batch_size'''			,       default=8			,       type=A			,       help='''Batch size per GPU/CPU for evaluation.'''			,       )
			parser.add_argument(
			    '''--recalculate'''			,       help='''Recalculate predictions even if the prediction file exists'''			,       action='''store_true'''			,       )
			parser.add_argument(
			    '''--num_beams'''			,       default=4			,       type=A			,       help='''Number of beams to be used when generating answers'''			,       )
			parser.add_argument('''--min_length'''			,       default=1			,       type=A			,       help='''Min length of the generated answers'''		)
			parser.add_argument('''--max_length'''			,       default=5_0			,       type=A			,       help='''Max length of the generated answers'''		)

			parser.add_argument(
			    '''--print_predictions'''			,       action='''store_true'''			,       help='''If True, prints predictions while evaluating.'''			,       )
			parser.add_argument(
			    '''--print_docs'''			,       action='''store_true'''			,       help='''If True, prints docs retried while generating.'''			,       )
			UpperCAmelCase_      :       Tuple          =       parser.parse_args()
			UpperCAmelCase_      :       List[str]          =       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''		)
			return args
def __UpperCAmelCase   (					A					:			Union[str, Any]		)    ->   int:
			UpperCAmelCase_      :       Union[str, Any]          =       {}
			if args.model_type is None:
						UpperCAmelCase_      :       Union[str, Any]          =       infer_model_type(args.model_name_or_path		)
						assert args.model_type is not None
			if args.model_type.startswith('''rag'''		):
						UpperCAmelCase_      :       Optional[Any]          =       RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
						UpperCAmelCase_      :       int          =       args.n_docs
						if args.index_name is not None:
									UpperCAmelCase_      :       int          =       args.index_name
						if args.index_path is not None:
									UpperCAmelCase_      :       int          =       args.index_path
			else:
						UpperCAmelCase_      :       str          =       BartForConditionalGeneration

			UpperCAmelCase_      :       Optional[int]          =       (
			    [f.path for f in os.scandir(args.model_name_or_path		) if f.is_dir()]
			    if args.eval_all_checkpoints
			    else [args.model_name_or_path]
			)

			logger.info('''Evaluate the following checkpoints: %s'''			,       A		)

			UpperCAmelCase_      :       Dict          =       get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
			UpperCAmelCase_      :       List[str]          =       evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

			for checkpoint in checkpoints:
						if os.path.exists(args.predictions_path		) and (not args.recalculate):
									logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path		)		)
									score_fn(A			,       args.predictions_path			,       args.gold_data_path		)
									continue

						logger.info('''***** Running evaluation for {} *****'''.format(A		)		)
						logger.info('''  Batch size = %d'''			,       args.eval_batch_size		)
						logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path		)		)

						if args.model_type.startswith('''rag'''		):
									UpperCAmelCase_      :       int          =       RagRetriever.from_pretrained(A			,       **A		)
									UpperCAmelCase_      :       List[str]          =       model_class.from_pretrained(A			,       retriever=A			,       **A		)
									model.retriever.init_retrieval()
						else:
									UpperCAmelCase_      :       List[str]          =       model_class.from_pretrained(A			,       **A		)
						model.to(args.device		)

						with open(args.evaluation_set			,       '''r'''		) as eval_file, open(args.predictions_path			,       '''w'''		) as preds_file:
									UpperCAmelCase_      :       Any          =       []
									for line in tqdm(A		):
												questions.append(line.strip()		)
												if len(A		) == args.eval_batch_size:
															UpperCAmelCase_      :       Union[str, Any]          =       evaluate_batch_fn(A			,       A			,       A		)
															preds_file.write('''\n'''.join(A		) + '''\n'''		)
															preds_file.flush()
															UpperCAmelCase_      :       int          =       []
									if len(A		) > 0:
												UpperCAmelCase_      :       Dict          =       evaluate_batch_fn(A			,       A			,       A		)
												preds_file.write('''\n'''.join(A		)		)
												preds_file.flush()

									score_fn(A			,       args.predictions_path			,       args.gold_data_path		)


if __name__ == "__main__":
						_UpperCamelCase						:						str  =					get_args()
						main(args)










'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


